Machine learning management method and machine learning management apparatus

A machine learning management method comprising:

• generating, by a processor, a plurality of first models by executing a model search according to each of a plurality of machine learning algorithms using first training data out of a plurality of sets of training data that have different sampling rates;
• calculating, by the processor and based on a prediction performance of each of the plurality of first models, an index value to be used to determine whether to generate each of a plurality of second models, which are generated by model searches according to the plurality of algorithms using a plurality of sets of second training data that are included in the plurality of training data but differ from the first training data, the index value being separately calculated for each of the plurality of second models;
• setting, by the processor and for each of the plurality of sets of second training data, a number of second models for which the index value is equal to or above a threshold, out of the second models generated using the second training data, as a priority for caching the second training data;
• deciding, by the processor when a model search has been executed using a new set of second training data that is not cached, whether to cache the new set of second training data based on the priority of the new set of second training data;
• and
• storing, when the deciding has decided to cache the new set of second training data, the new set of second training data in a memory.

A machine learning management apparatus comprising:

• a memory;
• and
• a processor configured to perform a procedure including:
• generating a plurality of first models by executing a model search according to each of a plurality of machine learning algorithms using first training data out of a plurality of sets of training data that have different sampling rates;
• calculating, based on a prediction performance of each of the plurality of first models, an index value to be used to determine whether to generate each of a plurality of second models, which are generated by model searches according to the plurality of algorithms using a plurality of sets of second training data that are included in the plurality of training data but differ from the first training data, the index value being separately calculated for each of the plurality of second models;
• setting, for each of the plurality of sets of second training data, a number of second models for which the index value is equal to or above a threshold, out of the second models generated using the second training data, as a priority for caching the second training data;
• deciding, when a model search has been executed using a new set of second training data that is not cached, whether to cache the new set of second training data based on the priority of the new set of second training data;
• and
• storing, when the deciding has decided to cache the new set of second training data, the new set of second training data in the memory.